skip to main content
10.1145/3025453.3025916acmconferencesArticle/Chapter ViewAbstractPublication PageschiConference Proceedingsconference-collections
research-article

Modeling Sub-Document Attention Using Viewport Time

Published: 02 May 2017 Publication History

Abstract

Website measures of engagement captured from millions of users, such as in-page scrolling and viewport position, can provide deeper understanding of attention than possible with simpler measures, such as dwell time. Using data from 1.2M news reading sessions, we examine and evaluate three increasingly sophisticated models of sub-document attention computed from viewport time, the time a page component is visible on the user display. Our modeling incorporates prior eye-tracking knowledge about onscreen reading, and we validate it by showing how, when used to estimate user reading rate, it aligns with known empirical measures. We then show how our models reveal an interaction between article topic and attention to page elements. Our approach supports refined large-scale measurement of user engagement at a level previously available only from lab-based eye-tracking studies.

References

[1]
David M Blei, Andrew Y Ng, and Michael I Jordan. 2003. Latent Dirichlet Allocation. Journal of Machine Learning Research 3, Jan (2003), 993--1022.
[2]
Agnieszka Bojko. 2006. Using Eye Tracking to Compare Web Page Designs: A Case Study. Journal of Usability Studies 1, 3 (May 2006), 112--120. http://dl.acm.org/citation.cfm?id=2835663.2835665
[3]
Georg Buscher, Ralf Biedert, Daniel Heinesch, and Andreas Dengel. 2010. Eye Tracking Analysis of Preferred Reading Regions on the Screen. In CHI '10 Extended Abstracts on Human Factors in Computing Systems (CHI EA '10). ACM, NY, NY, USA, 3307--3312.
[4]
Georg Buscher, Edward Cutrell, and Meredith Ringel Morris. 2009. What Do You See when You'Re Surfing?: Using Eye Tracking to Predict Salient Regions of Web Pages. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '09). ACM, NY, NY, USA, 21--30.
[5]
Christopher S. Campbell and Paul P. Maglio. 2001. A Robust Algorithm for Reading Detection. In Proceedings of the 2001 Workshop on Perceptive User Interfaces (PUI '01). ACM, NY, NY, USA, 1--7.
[6]
Mon Chu Chen, John R. Anderson, and Myeong Ho Sohn. 2001. What Can a Mouse Cursor Tell Us More?: Correlation of Eye/Mouse Movements on Web Browsing. In CHI '01 Extended Abstracts on Human Factors in Computing Systems (CHI EA '01). ACM, NY, NY, USA, 281--282.
[7]
Soussan Djamasbi, Marisa Siegel, and Tom Tullis. 2010. Generation Y, Web Design, and Eye Tracking. International Journal of Human-Computer Studies 68, 5 (May 2010), 307--323.
[8]
Laura Granka, Matthew Feusner, and Lori Lorigo. 2008. Eye Monitoring in Online Search. Passive Eye Monitoring (2008), 347--372.
[9]
Jeff Huang, Ryen White, and Georg Buscher. 2012. User See, User Point: Gaze and Cursor Alignment in Web Search. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '12). ACM, NY, NY, USA, 1341--1350.
[10]
Youngho Kim, Ahmed Hassan, Ryen W. White, and Imed Zitouni. 2014a. Comparing Client and Server Dwell Time Estimates for Click-level Satisfaction Prediction. In Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval (SIGIR '14). ACM, NY, NY, USA, 895--898.
[11]
Youngho Kim, Ahmed Hassan, Ryen W. White, and Imed Zitouni. 2014b. Modeling Dwell Time to Predict Click-level Satisfaction. In Proceedings of the 7th ACM International Conference on Web Search and Data Mining (WSDM '14). ACM, NY, NY, USA, 193--202.
[12]
Dmitry Lagun, Chih-Hung Hsieh, Dale Webster, and Vidhya Navalpakkam. 2014. Towards Better Measurement of Attention and Satisfaction in Mobile Search. In Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval (SIGIR '14). ACM, NY, NY, USA, 113--122.
[13]
Dmitry Lagun and Mounia Lalmas. 2016. Understanding User Attention and Engagement in Online News Reading. In Proceedings of the 9th ACM International Conference on Web Search and Data Mining (WSDM '16). ACM, NY, NY, USA, 113--122.
[14]
Dmitry Lagun, Donal McMahon, and Vidhya Navalpakkam. 2016. Understanding Mobile Searcher Attention with Rich Ad Formats. In Proceedings of the 25th Conference on Information and Knowledge Management (CIKM '16). ACM, NY, NY, USA, 599--608.
[15]
Pascual Martínez-Gómez, Tadayoshi Hara, and Akiko Aizawa. 2012. Recognizing Personal Characteristics of Readers using Eye-Movements and Text Features. Proceedings of COLING 2012 December (2012), 1747--1762. http://www.aclweb.org/anthology/C12--1107
[16]
Jan M Noyes and Kate J Garland. 2008. Computer- vs. paper-based tasks: are they equivalent? Ergonomics 51, 9 (2008), 1352--1375.
[17]
Radim Řehůřek and Petr Sojka. 2010. Software Framework for Topic Modelling with Large Corpora. In Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks. ELRA, Valletta, Malta, 45--50. http://is.muni.cz/publication/884893/en.
[18]
Selina Sharmin, Oleg Špakov, and Kari-Jouko Räihä. 2013. Reading On-screen Text with Gaze-based Auto-scrolling. In Proceedings of the 2013 Conference on Eye Tracking South Africa (ETSA '13). ACM, NY, NY, USA, 24--31.
[19]
Susanne Trauzettel-Klosinski and Klaus Dietz. 2012. Standardized assessment of reading performance: The new international reading speed texts IReST. Investigative Ophthalmology and Visual Science 53, 9 (2012), 5452--5461.
[20]
Eric W Weisstein. 2002. Erf. http://web.archive.org/web/20080207010024/http: //www.808multimedia.com/winnt/kernel.htm. (2002). Retrieved December 2016.
[21]
Xing Yi, Liangjie Hong, Erheng Zhong, Nanthan Nan Liu, and Suju Rajan. 2014. Beyond Clicks: Dwell Time for Personalization. In Proceedings of the 8th ACM Conference on Recommender Systems (RecSys '14). ACM, NY, NY, USA, 113--120.

Cited By

View all
  • (2024)Bridging the Analytics Gap: Optimizing Content Performance using Actionable Knowledge DiscoveryProceedings of the 35th ACM Conference on Hypertext and Social Media10.1145/3648188.3675121(185-192)Online publication date: 10-Sep-2024
  • (2023)Detecting the Disengaged Reader - Using Scrolling Data to Predict Disengagement during ReadingLAK23: 13th International Learning Analytics and Knowledge Conference10.1145/3576050.3576078(585-591)Online publication date: 13-Mar-2023
  • (2023)A Data-Driven Analysis of Behaviors in Data Curation ProcessesACM Transactions on Information Systems10.1145/356741941:3(1-35)Online publication date: 7-Feb-2023
  • Show More Cited By

Index Terms

  1. Modeling Sub-Document Attention Using Viewport Time

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Conferences
    CHI '17: Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems
    May 2017
    7138 pages
    ISBN:9781450346559
    DOI:10.1145/3025453
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

    Sponsors

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 02 May 2017

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. attention
    2. news articles
    3. reading
    4. user modeling
    5. web analytics

    Qualifiers

    • Research-article

    Conference

    CHI '17
    Sponsor:

    Acceptance Rates

    CHI '17 Paper Acceptance Rate 600 of 2,400 submissions, 25%;
    Overall Acceptance Rate 6,199 of 26,314 submissions, 24%

    Upcoming Conference

    CHI 2025
    ACM CHI Conference on Human Factors in Computing Systems
    April 26 - May 1, 2025
    Yokohama , Japan

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)27
    • Downloads (Last 6 weeks)2
    Reflects downloads up to 13 Feb 2025

    Other Metrics

    Citations

    Cited By

    View all
    • (2024)Bridging the Analytics Gap: Optimizing Content Performance using Actionable Knowledge DiscoveryProceedings of the 35th ACM Conference on Hypertext and Social Media10.1145/3648188.3675121(185-192)Online publication date: 10-Sep-2024
    • (2023)Detecting the Disengaged Reader - Using Scrolling Data to Predict Disengagement during ReadingLAK23: 13th International Learning Analytics and Knowledge Conference10.1145/3576050.3576078(585-591)Online publication date: 13-Mar-2023
    • (2023)A Data-Driven Analysis of Behaviors in Data Curation ProcessesACM Transactions on Information Systems10.1145/356741941:3(1-35)Online publication date: 7-Feb-2023
    • (2023)A Passage-Level Reading Behavior Model for Mobile SearchProceedings of the ACM Web Conference 202310.1145/3543507.3583343(3236-3246)Online publication date: 30-Apr-2023
    • (2022)Interactive Ad Avoidance on Mobile PhonesJournal of Advertising10.1080/00913367.2022.207726651:4(440-449)Online publication date: 27-Jun-2022
    • (2020)Brands as a nation: An analysis of overseas media engagement of top Chinese brandsGlobal Media and China10.1177/20594364209031105:1(22-39)Online publication date: 19-Feb-2020
    • (2020)Splitting the Web Analytics AtomProceedings of the 10th International Conference on Web Intelligence, Mining and Semantics10.1145/3405962.3405984(33-43)Online publication date: 30-Jun-2020
    • (2020)On Understanding Data Worker Interaction BehaviorsProceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3397271.3401059(269-278)Online publication date: 25-Jul-2020
    • (2020)A Price-per-attention Auction Scheme Using Mouse Cursor InformationACM Transactions on Information Systems10.1145/337421038:2(1-30)Online publication date: 27-Jan-2020
    • (2020)Efficient Neural Matrix Factorization without Sampling for RecommendationACM Transactions on Information Systems10.1145/337380738:2(1-28)Online publication date: 14-Jan-2020
    • Show More Cited By

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media